Evaluation of Hybrid Models to Estimate Chlorophyll and Nitrogen Content of Maize Crops in the Framework of the Future CHIME Mission

• Published in: Remote sensing (Basel, Switzerland) Vol. 14; no. 8; p. 1792
• Main Authors: Candiani, Gabriele, Tagliabue, Giulia, Panigada, Cinzia, Verrelst, Jochem, Picchi, Valentina, Caicedo, Juan Pablo Rivera, Boschetti, Mirco
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 08.04.2022
• Subjects: active learning; Agriculture; Algorithms; Canopies; Cereal crops; Chlorophyll; chlorophyll content; Common Agricultural Policy; Corn; Data transmission; Food security; Gaussian process regression; Hyperspectral imaging; Leaves; machine learning regression algorithm; Natural resources; Nitrogen; Nutritional status; radiative transfer modeling; Remote sensing; Retrieval; spaceborne imaging spectroscopy; Spectrum analysis; Vegetation; Italy; active learning; spaceborne imaging spectroscopy; machine learning regression algorithm; radiative transfer modeling; Gaussian process regression; chlorophyll content; nitrogen content
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs14081792

In the next few years, the new Copernicus Hyperspectral Imaging Mission (CHIME) is foreseen to be launched by the European Space Agency (ESA). This missions will provide an unprecedented amount of hyperspectral data, enabling new research possibilities within several fields of natural resources, including the "agriculture and food security" domain. In order to efficiently exploit this upcoming hyperspectral data stream, new processing methods and techniques need to be studied and implemented. In this work, the hybrid approach (HYB) and its variant, featuring sampling dimensionality reduction through active learning heuristics (HAL), were applied to CHIME-like data to evaluate the retrieval of crop traits, such as chlorophyll and nitrogen content at both leaf (LCC and LNC) and canopy level (CCC and CNC). The results showed that HYB was able to provide reliable estimations at canopy level (R = 0.79, RMSE = 0.38 g m for CCC and R = 0.84, RMSE = 1.10 g m for CNC) but failed at leaf level. The HAL approach improved retrieval accuracy at canopy level (best metric: R = 0.88 and RMSE = 0.21 g m for CCC; R = 0.93 and RMSE = 0.71 g m for CNC), providing good results also at leaf level (best metrics: R = 0.72 and RMSE = 3.31 μg cm for LCC; R = 0.56 and RMSE = 0.02 mg cm for LNC). The promising results obtained through the hybrid approach support the feasibility of an operational retrieval of chlorophyll and nitrogen content, e.g., in the framework of the future CHIME mission. However, further efforts are required to investigate the approach across different years, sites and crop types in order to improve its transferability to other contexts.